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Malaya COMPARISON OF CLUSTERING ALGORITHMS FOR DATA ANONYMIZATIONversityofZAHRA BIN TI MAHMOUD faculty OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY UNIVERSITY OF MALAYA KUALA LUMPUR 2019ay AA COMPARISON
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01
Determine the clustering algorithms you want to compare.
02
Select a dataset that will be used for the comparison.
03
Preprocess the dataset by handling missing values, normalizing data, and removing outliers.
04
Implement each clustering algorithm on the preprocessed dataset.
05
Evaluate the performance of each algorithm using metrics such as silhouette score, Davies-Bouldin index, or clustering purity.
06
Compare the results of each algorithm to determine which one performs the best for the given dataset.

Who needs a comparison of clustering?

01
Researchers in the field of machine learning and data mining who want to understand the strengths and weaknesses of different clustering algorithms.
02
Data scientists and analysts who want to choose the best clustering algorithm for a specific dataset and task.
03
Companies and organizations looking to improve their data analysis and decision-making processes through clustering techniques.
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A comparison of clustering is the process of grouping similar data points together based on certain criteria.
Any individual or organization conducting data analysis or research that involves clustering techniques may be required to file a comparison of clustering.
To fill out a comparison of clustering, one must provide detailed information about the data being analyzed, the clustering method used, and the results obtained from the analysis.
The purpose of a comparison of clustering is to organize and classify data in a meaningful way to uncover patterns and relationships.
A comparison of clustering should include details on the dataset, clustering algorithm used, number of clusters identified, and any insights or conclusions drawn from the analysis.
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